Explainable AI
Explainable AI (XAI) encompasses techniques that make AI system decisions understandable to humans. XAI is crucial for building trust, meeting regulatory requirements, and debugging model behavior.
Understanding Explainable AI
Explainable AI (XAI) encompasses methods and techniques designed to make the decisions and predictions of artificial intelligence systems understandable to humans. As deep learning models grow increasingly complex, their "black box" nature creates challenges for trust, accountability, and regulatory compliance in high-stakes domains like healthcare, finance, and criminal justice. XAI techniques include feature importance analysis from decision trees and ensemble methods, attention visualization in transformer models, LIME for local interpretability, and SHAP values grounded in game theory. Explainability is closely tied to responsible AI practices and is often a legal requirement under regulations like the EU AI Act. Beyond compliance, explainable AI helps data scientists debug models, identify data drift or bias, and build stakeholder confidence. The field bridges the gap between model performance and the transparency needed for real-world deployment.
Category
AI Ethics & Safety
Is AI recommending your brand?
Find out if ChatGPT, Perplexity, and Gemini mention you when people search your industry.
Check your brand — $9Related AI Ethics & Safety Terms
Adversarial Attack
An adversarial attack is a technique that creates deliberately crafted inputs designed to fool a machine learning model into making incorrect predictions. These attacks reveal vulnerabilities in AI systems and are critical to AI safety research.
Adversarial Training
Adversarial training is a defense strategy that improves model robustness by including adversarial examples in the training data. The model learns to correctly classify both normal and adversarially perturbed inputs.
AI Alignment
AI alignment is the research field focused on ensuring that AI systems pursue goals and behaviors consistent with human values and intentions. Alignment is considered one of the most important challenges in AI safety.
AI Ethics
AI ethics is the branch of ethics that examines the moral implications of developing and deploying artificial intelligence systems. It addresses fairness, transparency, privacy, accountability, and the societal impact of AI technology.
AI Safety
AI safety is the interdisciplinary field focused on ensuring AI systems operate reliably, beneficially, and without causing unintended harm. It encompasses alignment, robustness, interpretability, and governance research.
Bias in AI
Bias in AI refers to systematic errors or unfair outcomes in machine learning models that arise from biased training data, flawed assumptions, or problematic design choices. Addressing AI bias is essential for building fair and equitable systems.
Constitutional AI
Constitutional AI is an approach developed by Anthropic that trains AI systems to be helpful, harmless, and honest using a set of written principles. The model critiques and revises its own outputs based on these constitutional rules.
Deepfake
A deepfake is AI-generated synthetic media that convincingly replaces a person's likeness, voice, or actions in images, audio, or video. Deepfakes raise significant concerns about misinformation and identity fraud.